Evaluation of raindrop size distribution retrievals based on the … · 2013-01-16 · Evaluation...
Transcript of Evaluation of raindrop size distribution retrievals based on the … · 2013-01-16 · Evaluation...
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AATTMMOOSS
Evaluation of raindrop size distribution retrievals based on the Doppler spectra
using three beams
Remote-sensing of the environment (RSE)
Christine Unal
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AATTMMOOSS In this talk:
� Differential reflectivity Zdr cannot be used (near-vertical profiling or light rain) Doppler spectra
� first evaluation of rain Drop Size Distribution (DSD) comparing
retrievals from the same radar resolution volume using two different
polarizations
DelftUniversity ofTechnology
2Remote-sensing of the environment (RSE)
polarizations
� second evaluation comparing DSD retrievals in different directions
during stratiform light rain
TARA radarInfluence of the radial wind on the DSD estimates
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AATTMMOOSS
FM-CW Doppler-polarimetric S-band TARA radar
DelftUniversity ofTechnology
3Remote-sensing of the environment (RSE)
wind measurement available
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AATTMMOOSS
Doppler spectra model
Based on
Moisseev, Chandrasekar, Unal and Russchenberg, 2006: Dual-
polarization spectral analysis for retrieval of effective raindrop shapes
Input
- Drop Size Distribution (DSD) :
� median volume diameter D0
Output
DelftUniversity ofTechnology
4Remote-sensing of the environment (RSE)
� intercept parameter Nw
� shape parameter µ
- Radial wind (v0)
- Spectral broadening (σ0)
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AATTMMOOSS
Retrieval technique
Based on
Moisseev, Chandrasekar, Unal and Russchenberg, 2006: Dual-
polarization spectral analysis for retrieval of effective raindrop shapes
Non-linear optimization of D0 , µ , σ0
+ estimation of Nw , v0
DelftUniversity ofTechnology
5Remote-sensing of the environment (RSE)
+
=
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AATTMMOOSS
Sensitivity analysis
Parameter D0 Nw µ σ0 v0
Region 0.2 – 3 mm 0 – 8000 mm-1 m-3 -2 – 10 0.1 – 0.9 m s-1 0 – 1.2 m s-1
RMSD 0.12 mm 1350 mm-1 m-3 0.67 0.04 m s-1 0.18 m s-1
CV(RMSD) 17% 54% 8.4% 28%
Simulation results on averaged Doppler spectra (10-55 dBZ reflectivity, average:30)
DelftUniversity ofTechnology
6Remote-sensing of the environment (RSE)
CV(RMSD) 17% 54% 8.4% 28%
Parameter Z LWC Nt
RMSD 0.30 dB 0.13 g m-3 142 m-3
CV(RMSD) 0.91% 22% 7.4%
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AATTMMOOSS
Multi-beam retrieval example (non-averaged Doppler spectra)
Vertical beam75 deg 75 deg 69 deg
DelftUniversity ofTechnology
7Remote-sensing of the environment (RSE)
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AATTMMOOSS
Multi-beam retrieval profile example
Vertical
Median volume diameter D0 profile in 3 different looking directions
Retrieval procedure + height-smoothing
DelftUniversity ofTechnology
8Remote-sensing of the environment (RSE)
Vertical beam
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AATTMMOOSS
Retrievals comparison (light rain)
HH and VV non averaged Doppler spectra
D0 Nw µ v0
DelftUniversity ofTechnology
9Remote-sensing of the environment (RSE)
σ0 Z
LWC
Nt
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AATTMMOOSS
Dynamical retrieval v0
Comparison between retrieval of radial wind (-v0) and radial component of mean horizontal wind measured by TARA
DelftUniversity ofTechnology
10Remote-sensing of the environment (RSE)
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AATTMMOOSS
Influence of the error on v0 on the DSD retrieval
Doppler spectra are shifted using TARA mean
DelftUniversity ofTechnology
11Remote-sensing of the environment (RSE)
Doppler spectra are shifted using TARA mean horizontal wind measurement before retrieval
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AATTMMOOSS Influence of the error on v0 on the median volume diameter (D0)
Mean horizontal wind correction before retrieval1600 m
1400 m
DelftUniversity ofTechnology
12Remote-sensing of the environment (RSE)
800 m
400 m
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AATTMMOOSS
Conclusions
� development of an automatic rain retrieval technique based on the complete Doppler spectrum (from drizzle to heavy rain)
� a tool for study cases
� consistency of the retrievals (D0, µ, v0, σ0, LWC) representing the same radar resolution volume
DelftUniversity ofTechnology
13Remote-sensing of the environment (RSE)
� when Zdr is too small (light precipitation, near-vertical profiling), it looks necessary to input the radial component of the mean horizontal wind to reduce the errors on (D0, Nw, µ)
� adding two other looking directions (beams) to estimate the contribution of the radial wind may solve this problem
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AATTMMOOSS
Moisseev, Chandrasekar, Unal and Russchenberg, 2006: Dual-polarization spectral analysis for retrieval of effective raindrop shapes
DelftUniversity ofTechnology
14Remote-sensing of the environment (RSE)
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AATTMMOOSS
Do
σ0
µ
+
+
Do min Do max
σo min σo max
µ maxµ min
L
L
L
Fitting
Do
σ0
µ
�Ite
rativ
e se
lect
ion
proc
edur
e
�O
ptim
izat
ion
pro
cedu
refr
om c
ost f
unct
ion
stud
y (L
)
� Final outcome( ) ( )
max
min
2mod0 , , 0( ) ,
vmeasHH dB HH dB
v v
L D sZ v sZ v D=
= − ∑
( )max
0min
2mod
0 , , 0( ) ( , )v
measHH dB HH dB D
v v
L sZ v sZ vσ σ=
= − ∑
DelftUniversity ofTechnology
15Remote-sensing of the environment (RSE)
Nw
V0
+
measured spectrum
Fitting
Model spectrum
�es
timat
ion
proc
edur
e
� Output of the model
Max cross-correlation
Variable projection method (Rust, 2003)
Error calculationNon linear least-square algorithm
Estimation from Do, σ0 and µ
+ ( ) ( )max
0 0min
2mod
, , ,( ) ,
vmeasHH dB HH dB D
v v
L sZ v sZ vσ
µ µ=
= − ∑
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AATTMMOOSS
Retrieval outputs: dsd, v0 and σσσσ0
� median volume diameter D0
� µ bounded [-1 5]
� spectral broadening σ0 bounded [0 1] m s-1
�intercept parameter Nw free
� radial wind v0 free
DelftUniversity ofTechnology
16Remote-sensing of the environment (RSE)
crucial to get the actual D0
help
Polarimetry with Zdr
Wind estimates with v0
Reflectivity
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AATTMMOOSS
Multi-beam raindrop size comparison (Z-D0)
Mean horizontal wind correction before retrieval1600 m
1400 m
DelftUniversity ofTechnology
17Remote-sensing of the environment (RSE)
800 m
400 m
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AATTMMOOSS
Multi-beam raindrop size comparison (Nw)
Mean horizontal wind correction before retrieval1600 m
1400 m
DelftUniversity ofTechnology
18Remote-sensing of the environment (RSE)
800 m
400 m
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AATTMMOOSS
Multi-beam raindrop size comparison (µµµµ)
Mean horizontal wind correction before retrieval
1600 m
1400 m
DelftUniversity ofTechnology
19Remote-sensing of the environment (RSE)
800 m
400 m